Research Article
Regret-Optimal Online Caching for Adversarial and Stochastic Arrivals
@INPROCEEDINGS{10.1007/978-3-031-31234-2_9, author={Fathima Zarin Faizal and Priya Singh and Nikhil Karamchandani and Sharayu Moharir}, title={Regret-Optimal Online Caching for Adversarial and Stochastic Arrivals}, proceedings={Performance Evaluation Methodologies and Tools. 15th EAI International Conference, VALUETOOLS 2022, Virtual Event, November 2022, Proceedings}, proceedings_a={VALUETOOLS}, year={2023}, month={5}, keywords={Online caching algorithms regret bounds}, doi={10.1007/978-3-031-31234-2_9} }- Fathima Zarin Faizal
Priya Singh
Nikhil Karamchandani
Sharayu Moharir
Year: 2023
Regret-Optimal Online Caching for Adversarial and Stochastic Arrivals
VALUETOOLS
Springer
DOI: 10.1007/978-3-031-31234-2_9
Abstract
We consider the online caching problem for a cache of limited size. In a time-slotted system, a user requests one file from a large catalog in each slot. If the requested file is cached, the policy receives a unit reward and zero rewards otherwise. We show that a Follow the Perturbed Leader (FTPL)-based anytime caching policy is simultaneously regret-optimal for both adversarial and i.i.d. stochastic arrivals. Further, in the setting where there is a cost associated with switching the cached contents, we propose a variant of FTPL that is order-optimal with respect to time for both adversarial and stochastic arrivals and has a significantly better performance compared to FTPL with respect to the switching cost for stochastic arrivals. We also show that these results can be generalized to the setting where there are constraints on the frequency with which cache contents can be changed. Finally, we validate the results obtained on various synthetic as well as real-world traces.
